Comparative rates of text reuse in classical Latin hexameter
poetry

Abstract

This paper presents a quantitative picture of the interactions between poets in
the Latin hexameter tradition. The freely available Tesserae website (tesserae.caset.buffalo.edu) automatically searches pairs of texts in
a corpus of over 300 works of Latin literature in order to identify instances
where short passages share two or more repeated lexemes. We use Tesserae to survey relative rates of text reuse in 24
Latin hexameter works written from the 1st century
BCE to the 6th century CE. We compare the
quantitative information about text reuse provided by Tesserae to the scholarly tradition of qualitative discussion of
allusion by Latinists.

The detection and interpretation of allusion currently represent the dominant mode of
study of Latin poetry.[1] The typical goal of
intertextual study is to describe how links between texts affect the meaning of both
the specific passages that contain them and the poems as a whole. Although
intertextual associations may be signalled in many different ways (including
similarity of action, character, or theme), verbal repetition, or text reuse, is the
best studied and often the strongest type of signal. Philogical commentaries,
copiously detailed collections of information on individual books of Latin epic
poems, have been the traditional means for Latin poetry scholars to collect and
present interpretations based on studies of text reuse. An example from Parkes’
recent commentary on the fourth book of Statius’ Thebaid demonstrates the practice of translating the evidence of verbal
repetition into interpretation:

This exemplary note builds its interpretation on the evidence of the
repetition of two key lexemes, the verb percutio
("I strike") and the noun amor ("desire").[3] The cooccurence of these lexemes
in the Statian passage signifies for most readers a link to the passage from Vergil.
The discovery of such verbal links has been facilitated in recent years by digital
tools such as the freely available Tesserae web
interface (tesserae.caset.buffalo.edu), a search program developed by Neil Coffee and a
team at the University at Buffalo. Tesserae allows
users to search pairs of texts (an earlier "source" text paired
with a later "target" text) in a corpus of over 300 poetic and
prose works, in order to discover every instance where short passages (either lines
of verse or grammatical periods) share two or more repeated lexemes. Thus, a Tesserae search that pairs the Thebaid with the Aeneid permits the user
to discover the allusion discussed by Parkes by identifying the repetition of the
lexemes percutio and amor. The Tesserae scoring system
signals the potential interpretive significance of the match by assigning it a high
score, 8 out of approximately 11.[4]

The words in the Aeneid are spoken by Allecto, a demon
of the underworld, and we may thus once more translate this evidence of verbal
repetition provided by Tesserae into literary
interpretation.[5] Parthenopaeus’ desire to fight in the Theban war in
Statius is not only fatal, like the desire of Vergil’s Euryalus to participate in
Nisus’ expedition; it is also infernal, like the war provoked by Vergil’s Allecto.
This is consistent with Statius’ characterization of the Theban war as destructive
and impious throughout the Thebaid. Such new avenues
for specific intertextual interpretation are the typical results of Tesserae searches. Previous examples of comparable results
can be found in a study of verbal reuse of Vergil’s Aeneid by the epic poet Lucan [Coffee et al. 2012]. Coffee
et al. hand-ranked all Tesserae results from a comparison of Lucan Bellum
Civile 1 (target) and Vergil’s Aeneid
(source) on a 5–point scale of interpretive significance. They concluded that the
Tesserae search had identified 25% more
interpretively significant instances of text reuse than the standard philological
commentaries on Bellum Civile 1 [Roche 2009]
[Viansino 1995].

The interpretation of specific allusions relies partly on the characterization of the
overall intertextual relationship between texts, which is often hampered by a
haphazard approach to gathering data. This paper presents a more consistent,
quantitative picture of the interactions between poets in the Latin hexameter
tradition. We use Tesserae to generate a statistical
analysis of relative rates of text reuse in 24 Latin hexameter works written from
the 1st century BCE to the 6th century CE. We then compare the quantitative information about text
reuse provided by Tesserae to the scholarly tradition
of qualitative discussion of allusion by Latinists. Statistical analyses of certain
aspects of Latin poetry are not new. Drobisch’s studies beginning the 1860s
represented the birth of the modern statistical studies of metrical aspects of the
epic hexameter, a tradition which has reached a high-water mark in the recent work
of Ceccarelli [Ceccarelli 2008]
[Drobisch 1866]. Counts of individual lexical items in Latin poetry,
usually in an effort to determine whether particular words should be considered
"poetic" or "unpoetic", are best
represented by the tradition of Axelson’s work [Watson 1985]
[Axelson 1945]. Yet scholars have not typically evaluated instances of
verbal reuse in quantitative terms, as it has simply not been possible for human
readers to count such instances accurately. The speed, consistency, and
comprehensiveness of Tesserae searches now enable the
interpreter to quantify the reuse of phrases on a scale beyond the capacities of
ordinary human reading.

Powerful and productive as the Tesserae interface is,
the following limitations must be clearly understood. They bear on analysis of
specific passages, and to a lesser extent on our large-scale study:

Text reuse does not give the full, complex picture of intertextuality in Latin
hexameter, where allusions may be signalled by similarity of action, character,
theme, and so on.

Not all text reuse features the repetition of two or more lexemes. At its
current stage of development, Tesserae focuses on
pairs of lexemes and so cannot reliably identify repetition of single
significant words. It would accordingly be unable to flag, for example, the very
common word arma ("warfare"). This word
takes on a new intertextual significance in poems written after the Aeneid, a foundational epic poem that begins with the
words Arma uirumque cano… ("I sing of arms and the man…") [Fowler 1997, 20].
There is accordingly need of a sensitive human interpreter to uncover the
metapoetic significance, for example, of the opening word of Ovid’s Amores, Arma graui numero uiolentaque bella parabam / edere…
("I was beginning to sing of arms and violent
wars in a serious meter...")

The Latin poets wrote for an audience of Roman elites that were literate in
Greek [Hutchinson 2013], and so created numerous translingual
calques on Greek phrases. To remain with the example of Vergil, the Aeneid adapts numerous lines and phrases from Homer’s
Iliad and Odyssey.
Some foundational studies have uncovered these calques using traditional
philological methods [Knauer 1964]
[Nelis 2001], but such studies have not been pursued
systematically across the Latin corpus. A feature of Tesserae currently in development searches for such translingual
allusions between Latin and Greek poetry, but is not yet a reliable tool.

Repetitions with verbal variations that seem slight to a human reader are
determinative for Tesserae. For example, Tesserae will locate the following correspondence
based on the repetition of the lexemes Acheron
and moueo:

The change from the verb moueo ("I move") to uideo
("I see") means the phrase no longer contains two
repeated lexemes. This means that Tesserae will
inevitably miss some of the variations on a verbal motif that form a component
of the Latin poets’ creative art. That said, the majority of allusions
identified via traditional reading are repeated phrases. So though Tesserae cannot uncover allusions of this type, the
majority of such allusions are typically missed by human readers as well.

The Tesserae scoring system provides a measure of
interpretive significance that correlates with human-generated measures [Forstall et al. 2014]. Numerous passages of Latin poetry that human
readers have traditionally thought of as linked through allusion are also
high-scoring lexeme matches, and these correspondences form the basis for
scholarly confidence in the scoring system. Yet the score assigned to any given
lexeme match does not generate by itself the kind of sensitive assessment of
significance that a scholarly reader of Latin poetry brings to the
identification of parallel passages. In order to be significant, the allusion
must be placed in a larger scholarly narrative of the passage’s compositional
goals. A human reader must be able to make a plausible interpretation of the
allusion before it can be recognized as an allusion rather a chance repetition
[Farrell 2005]. Tesserae’s
usefulness comes in discovering potential allusive connections through lexeme
matching and ordering them by the rarity and proximity of the paired lexemes.
Subjective interpretation of these connections is still required for any
meaning-making exercise [Drucker 2009].

Within these acknowledged limitations, Tesserae can be
an extraordinarily powerful tool for representing the large-scale reuse of text in a
literary tradition. Focusing as it does on repetition of phrases, the most commonly
studied marker of allusion, Tesserae can provide a
large-scale view of intertextual relationships that models traditional scholarly
practice. The program can generate provisional answers to questions of particular
relevance to the study of the Latin hexameter genre. Tesserae
enables us to undertake the first large-scale statistical study of
intertextuality in classical literary studies. Classicists have used new digital
tools since their inception, and several techniques of digital text analysis were
pioneered on Latin literary corpora, from Fr. Busa’s Index
Thomisticus to the Packard concordance of Livy [Bodard and Mahony 2010]
[McCarty 2005]. Studies of intertextuality, however, have generally
been confined to pairs or very small sets of texts, and have traditionally relied on
broad but subjective classification of intertextual data (synonyms, similar motifs,
images, etc.), rather than objective parameters such as lexeme matches, lexeme
frequency, and lexeme proximity. The Tesserae scoring
system, however, represents the first opportunity to quantify the study of
intertextuality using a large set of poems and objective parameters. Our object of
study is the entire super-genre of Latin hexameter poetry, in which we privilege the
system of relationships between texts rather than any integral text itself.

Latin poetry scholars have traditionally divided the "super-genre"
of hexameter into several subgenres, including satire, epic, and didactic [Hutchinson 2013]. Is it possible to quantify the verbal cohesiveness
and distinctiveness of these genres? What other general factors affect text reuse
across the entire hexameter tradition? Can the well-known influence of Vergil and
Ovid on their epic successors be quantified? In particular, can it be determined how
frequently one predecessor’s text is reused compared to another’s? For example, is
Statius’ Thebaid more "Vergilian" in
terms of text reuse than another contemporary epic poem, Silius Italicus’ Punica? Most specialist readers of these Flavian epic
poets would correctly guess that the answer is no, but would perhaps not be so
confident in making assertions about the two poems’ relative rates of reuse of
other, earlier poets such as Ovid, Lucan, or Manilius. Which works in the classical
hexameter tradition provide the most significant verbal resources for the hexameter
epics of late antiquity? This study offers preliminary answers to such questions
from a quantitative perspective by surveying the relative rates of text reuse in 24
Latin hexameter works written from the 1st century BCE
to the 6th century CE.

2. METHODS

a. Text Selection

Our analysis included every possible source–target pair from a set of 24
Latin hexameter texts written from the 1st
century BCE to the 6th century CE (Table 1[6]). This set
included every hexameter text available on the Tesserae website,[7]
excluding hexameter poems from polymetric collections (such as Catullus’
poems or Statius’ Silvae), hexameter works with
non-hexameter prefaces (such as Claudian’s In
Rufinum[8]), and four very short minor texts.[9]

We then partitioned the results by score. Tesserae assigns each matched phrase a score (rounded to the
nearest integer) according to the following formula, which reflects the
observation that instances of text reuse featuring rare words in close
proximity are often more interpretively significant than instances featuring
common words spaced farther apart [Forstall et al. 2014]
[Coffee et al. 2013].

c. Weighing of counts

We thus obtained for each pair a count of the number of hits at each score
(from 2 to 11). Hits scoring 6 and lower were excluded from the analysis,
since it has been shown that these are unlikely to be instances of
interpretively significant text reuse [Forstall et al. 2014]. We were
left with five data points for each pair, C7, C8, C9, C10, and
C11 (counts of score 7, 8, 9, 10,
and 11; Table 8 and 9). In order to convert these five counts into a single useful
"composite count", C, we took advantage
of the strongly linear relationships between counts of every score except
for the rare C11 hits. Because the
mean correlation was strongest between C9 and the other counts (mean R2 = 0.879; mean ρ = 0.931), the smallest amount of error was
introduced by converting all counts into C9, using a combination of linear regressions and principal
component analysis.

First, we used a series of linear regressions to characterize the
relationship between C9 and the other
four counts and obtain an initial composite count, Cregr.[12]Second, we applied principal component analysis (PCA) to
the five counts, first correcting for their very different scales by
dividing each count by its standard deviation, in order to obtain a second
composite count, Cpca.[13] Noting the similar weights in the formulae for Cregr and Cpca, we chose the average weights for the
final formula for composite counts, which we considered to be the “observed
count”, Cobs:

Figure 7.

d. Relative intensity of reuse

The resulting observed counts could not be directly compared to one another,
since the total lengths of the texts were different for each source–target
pair. For instance, we expected to obtain a much higher Cobs value for
the pair Ovid, Metamorphoses (78098 words) – Silius
Italicus, Punica (76292 words) than for the pair
Horace, Ars Poetica (3090 words) – Claudian, De Bello Gildonico (3165 words), simply because there
is much more space for text reuse in the longer texts. Indeed, we found that
Cobs was correlated with the lengths of both source and target
texts, Ws and Wt; the correlations were strongly linear
when the variables were converted to a logarithmic scale (cobs, ws,
and wt).

Thus, we could use a multiple regression to determine (in logarithmic scale)
an expected count, cexp, for any given length of source and
target text, ws and wt. We obtained the model (R2 = 0.979):[14]

Figure 9.

We then subtracted the expected count for each source–target pair
from the observed count to obtain a residual, which we considered to be a
measure of the relative intensity of text reuse for each pair:

A positive value of r for a given pair indicates that
the observed intensity of text reuse was higher than would be expected for
an "average" pair of texts with those particular word
counts — that is, for a pair of texts with no particularly strong or weak
intertexual relationship. A negative value of rindicates that the observed intensity of text reuse was lower than
average. The further the value deviates from zero, the stronger the evidence
for an intensity of reuse above or below average. Thus, we sorted all pairs
by their r values, presented in both standardized and
non-standardized forms (Table 3).[15] We also presented the
(non-standardized) r values graphically, partitioning
the pairs by source text (Figure 11) and
target text (Figure 12), and presented various
subsets of the data to aid discussion (Figures
13–15, Tables 5–7).

It should be reiterated that r is not a measure of the
number of phrases reused for each pair (for which Cobs is the most
direct measure), but a measure of the intensity of
text reuse that takes into account the lengths of the source and target
texts in each pair. For instance, the very high Cobs value of 7407.3 for the
pair of the longest texts in our data set, Ovid, Metamorphoses (78098 words) – Silius Italicus, Punica (76292 words), actually reflects only moderately intense
text reuse (r = 0.146), whereas the very intense
reuse (r = 1.280) of Vergil’s Georgics (14154 words) by Vergil’s later poem, the Aeneid (63719 words) corresponds to a lower Cobs
value (1974.8) because the texts are shorter.

e. Centrality

For each of our 24 chosen texts, we determined the mean value of r for all pairs involving that text (23 pairs each
time), and sorted the texts by the results (Table
4). We considered this to be a measure of the
"centrality" of each of our chosen texts within the
24–text set: that is, how often each text reuses earlier texts and is reused
by later texts. A text strongly influenced by its predecessors and
influential to its successors would have a higher mean r than a text more peripheral to the literary tradition of Latin
hexameter poetry.

3. RESULTS AND DISCUSSION

We have kept two objectives in mind in interpreting our data set. First, we
attempt to test whether the results of the automated search and statistical
analysis match the conclusions reached by traditional scholarship. Second, we
endeavor to identify unexpected results that suggest avenues for future
research. We achieve those two objectives when interpreting both general
(sections 3.a-b) and specific trends (section 3.c).

a. Statistical outliers and centrality

Three pairs with standardized residuals near or above |3| may be considered
statistical outliers (Georg – Aen, standardized r = 4.571; Met – Mos, 3.830; Ars – Gild, –2.977). These
results reflect several phenomena that we will discuss: the influence of
author on text reuse (Georg – Aen, section 3.b), the influence of genre (Ars – Gild, 3.b), and the importance of
Ovid (among others) to late antique hexameter (Met –
Mos, 3.c.iv). For a further 11 pairs,
standardized residuals near or above |2| indicate intensity of text reuse
markedly above or below average; these results also reflect phenomena that
we will discuss.[16] These
standard statistical thresholds should not be relied upon naively, however:
for instance, several pairs for which we would expect a strong intertextual
engagement (such as texts written by the same authors) had standardized r values well below 2.

The centrality scores conformed to expectations (Table 4). The high centrality of the Aeneid (0.133) reflects the importance of Vergil’s works to the
subsequent hexameter tradition, while the high centrality of the Ilias Latina (0.186) reflects multiple reuse
facilitated by its intense reuse of the Aeneid
(see section 3.c.i). The high centrality of the Georgics (0.279) stems from a combination of these factors. All
four of Claudian’s works had positive centrality. This reflects not only
Claudian’s extensive reuse of his predecessors, but also the influence of
authorship on text reuse: each of Claudian’s works had high r values when paired with the other three works, thus
increasing their centrality. The low centrality of the works of Horace,
Persius, Juvenal, and Lucretius reflects the influence of genre in our data
set comprising mainly epic/panegyric texts. Perhaps the most unexpected
result is the high centrality of the Achilleid
(0.117), which reflects both intense reuse of earlier epic sources and
intense reuse by later epic targets. Because the Achilleid is a very short text, certain considerations must be
kept in mind (see section 3.c.i).

b. General trends

Unsurprisingly, the most important influence on text reuse intensity was
authorship. In all 13 cases where a pair of texts was written by the same
author, the reuse intensity was higher than average (r > 0.000), markedly so in 5 of the cases (standardized r > 2.000); see Figure
13 and Table 5. Vergil showed the
highest intensity of text reuse within his own poems, followed by Claudian,
while Horace and Statius reused their own poems with less intensity. Though
drawing on a very different data set (a relatively small corpus of Latin
hexameter poems), the results are nevertheless broadly comparable to
Jockers’ study of the author signal in a corpus of 3500 nineteenth-century
novels written in English. Jockers observes that of five "signals" (author, decade, genre, gender, and text),
the author signal is the strongest.[17]

A secondary influence on text reuse intensity was genre. Although
categorizing Latin poetry by genre is difficult, we may obtain a rough idea
of the influence of genre by partitioning the texts of our data set into
three genres: didactic, epic/panegyric, and satiric (Figure 14).[18] Within the small didactic and
satiric genres, reuse intensity was higher than average for 5 of 6 pairs
(r > 0.000; the exception, HSat – JSat, was
slight: r = −0.007). Within the much larger (and more
diverse) epic/panegyric genre, reuse intensity was higher than average in 66
of 78 pairs; the 12 remaining pairs had only slightly lower than average
reuse intensity (standardized r ≥ −0.446). In
contrast, pairs comprising texts from different genres tended to display
lower than average reuse intensity. The trend was clearest for pairs
composed of one epic/panegyric and one satiric text: 37 of 39 pairs had
lower than average reuse intensity.[19] The results conform to the expectations of traditional
reading, as epic and satire are the most distant hexameter genres from one
another in style and subject matter. Genre is also perhaps the best
explanation for the trends seen in the "centrality"
measure (Table 4). Since 13 of 24 texts in our
data set belong to the epic/panegyric genre, we would expect each of them to
be more central than texts belonging to smaller genres. This is true in most
cases; the most notable exception is the Georgics, which had the highest centrality score by far,
despite belonging to the didactic genre. We discuss this exceptional text in
section 3.c.i.

Time period appeared to have no influence on text reuse intensity. This is
not surprising, since the technical and aesthetic constraints of hexameter
poetry discouraged changes in diction or syntax over time. However, it is
possible that a future study which controls for much more salient influences
such as authorship and genre may discover a subtle influence of time
period.

c. Specific observations

The 276 pairs in our data set represent a generically and chronologically
diverse collection of texts. Different scholars will accordingly highlight
various aspects of the data. We only offer a handful of specific
observations here. As with the general trends we observed, these specific
results both confirmed that our analysis falls in line with the results of
traditional scholarship and identified several possible avenues for future
inquiry. For instance, Virgil’s Aeneid
predictably emerged as a major influence on subsequent poetry of all
periods. Lucretius’ De Natura Rerum was not a
prominent verbal resource for later authors. The four Flavian epics were
closely related, and late antique poets reused material from previous works
in expected ways. The congruence of these results with traditional
scholarship supports our contention that several unexpected results are
indicators of potential for fruitful further research. For instance,
Virgil’s Georgics and the anonymous Ilias Latina scored high in reuse intensity in
almost every case. This is probably an indication of frequent multiple
allusions to both these texts and the more prominent Aeneid and Metamorphoses (section
3.c.i). The relationship between the Flavian epics and the Aeneid appears to be more
"creative" or "original" than
often allowed, although these terms must be used carefully (see 3.c.ii).
Horace’s Ars Poetica seems to have an
unexpected influence on Manilius’ Astronomicon,
suggesting that didactic sensibility may cut across genre (see 3.c.iii).
Finally, Ausonius’ Mosella, usually considered
primarily "Vergilian" in nature, also shows close links
with Ovid’s Metamorphoses (3.c.iv).

i. Vergil’s Georgics and the Ilias Latina

The influence of Vergil’s Aeneid on the
subsequent tradition of Latin hexameter is well established and reflected in
our results. The work had a high centrality score (0.133) and higher than
average reuse intensity (r > 0.000) when paired
with 13 of 18 subsequent target texts (the exceptions are BC and the non-epic texts Ars, PSat, JSat, and HE); see Tables 4 and 6 and
Figure 11. However, the results for
Vergil’s early work, the Georgics, are even
more exceptional. Its centrality score was more than twice as high (0.279)
and it had higher than average reuse intensity (r
> 0.000) when paired with 16 of 20 subsequent target texts (the
exceptions are the non-epic texts Ep, Ars, PSat, and
JSat). These results may seem surprising at
first. Although the Georgics is an important
text, few would argue that its influence on subsequent Latin literature
eclipses that of the Aeneid. But two factors
must be kept in mind. First, recall that r is not a
measure of the number of phrases reused for each pair (for which Cobs is the most direct
measure), but a measure of the intensity of text reuse that
takes into account the lengths of the texts in each pair. Because the Aeneid is much longer than the Georgics (63719 vs 14154 words), it requires
values of Cobs over 7 times
higher, and thus the reuse of many more phrases, in order to achieve the
same residual when paired with any subsequent target text. Subsequent target
texts use many more phrases from the Aeneid
than from the Georgics in total,[20] and the influence of the Aeneid on subsequent literature is therefore more obvious to the
reader. Yet the intensity of the reuse is greater for the shorter Georgics.

The second factor arises from Vergil’s extensive reuse in the Aeneid of his own phrases from the Georgics, which resulted in the highest r value in our data set (1.280), one of three
statistical outliers (standardized r = 4.571).
Because Vergil’s two texts share many phrases, subsequent target texts that
reuse phrases from one Vergilian text will often automatically reuse the
same phrase from the other Vergilian text. In practice, subsequent epic
poems that reuse phrases from the epic Aeneid
will often automatically reuse the same phrase from the Georgics. A similar phenomenon explains the unexpected results
for the Ilias Latina. Although no scholar would
argue that this minor poem, a rough compression and translation of the
Iliad, exerted any discernable influence on
Latin literature in antiquity,[21] it
had a higher centrality score than the Aeneid
(0.186) and higher than average reuse intensity (r
> 0.000) when paired with every subsequent target text (Tables 4 and 6 and
Figure 11). However, the Ilias Latina also had markedly higher than average
reuse intensity (standardized r > 2.000) when
paired with both the Aeneid and Ovid’s Metamorphoses, two foundational texts for later
Latin literature.[22] This suggests that when a
subsequent target text reuses phrases from either the Aeneid or the Metamorphoses, it
will often automatically reuse the same phrase from the Ilias Latina and thereby increase the r
value when paired with that poem.

The high scores for both the Georgics and
Ilias Latina demonstrate that allusion in
Latin literature is not always a case of a target text reusing a phrase from
a single, specific source text. On the contrary, an allusion to, say, the
Aeneid often necessarily entails an
allusion to the Georgics, the Ilias Latina, or some other text(s). While
scholars routinely privilege one source text at the expense of the others
for the sake of interpretation, the automatic searches of Tesserae do not. This egalitarian interpretive
practice is not very suitable in the case of the Ilias
Latina, a minor text rightly subordinated to the sources it
reuses, but it is more suitable in the case of the Georgics, where readers will more often hit upon compelling
interpretations by treating the Georgics as a
source text on par with the Aeneid.[23]Tesserae encourages this kind of interpretation
not only by presenting all texts as equal in value, but also by offering the
option to perform multi-text searches (http://tesserae.caset.buffalo.edu/multi-text.php), where matches
between a source–target pair are presented alongside every other instance of
the matching phrase in a user-selected set of texts.

ii. Post-Vergilian classical epic

Scholarly interest in post-Vergilian classical epic (the Metamorphoses, Bellum Civile,
Argonautica, Thebaid, Achilleid, and Punica) has roughly tracked the chronology of the
epics themselves, with attention paid first to the Metamorphoses and last to the Punica. Similarly, the assumption has often been made that the
earlier epics (Metamorphoses and Bellum Civile) responded to Vergil’s influence in
more creative and original ways, while the four later epics of the Flavian
period tended to imitate Vergilian epic less creatively.[24] To compare this assumption to
the results of our study, we must bear in mind the nature of the text reuse
that Tesserae can discover. At its current
stage of development, Tesserae identifies only
matching phrases with exact repetition of two or more lexemes. It cannot
detect allusions signaled by similarity of action, character, or theme, or
text reuse involving single significant words or verbal variations. That is,
Tesserae preferentially detects exactly the
sort of allusions that may be classified as less
"creative". Thus a high residual indicates not only
higher than expected text reuse, but also potentially a less
"creative" allusive relationship.

Bearing this in mind, the results do not fully support the assumption of
declining creativity over time (Figure 15 and
Table 7). In contrast, although the
intensity of text reuse of both the Georgics
and Aeneid by the Argonautica, Thebaid, and Achilleid was higher than average (0.160 ≤ r ≥ 0.299), it was not as high as the intensity of
reuse of any of Vergil’s three works by the Metamorphoses (0.323 ≤ r ≥ 0.560). The
intensity of reuse of Vergil by the Bellum
Civile was even lower: in fact, the intensity of reuse of the
Aeneid was slightly lower than average (r = −0.026).[25] Thus, it would seem that the intertextual
engagement with Vergil’s texts by Lucan, Valerius Flaccus, and Statius are
either less intense or more "creative" (or both) than
often assumed.

The notable exception is the Punica of Silius
Italicus, which had much higher than average intensity of text reuse when
paired with the Georgics (r = 0.433) and Aeneid (r = 0.540). This is consistent with the assumption of
an uncreative intertextual relationship, and inconsistent with recent claims
about the Punica’s originality.[26] It must be acknowledged, however, that
"originality" and "creativity" are
subjective concepts, which are not directly measured by r values. A high r value for a given pair
indicates only that the number of matching phrases of two or more lexemes
was greater than expected for an "average" pair of texts
with the same word counts. It does not indicate, for instance, a paucity of
other kinds of subtler intertextuality (text reuse with verbal variation, or
similarities of action, theme, or character). Nor does it take into account
the context into which the lexemes are redeployed: a poet may, for instance
quote a predecessor’s words exactly, but in a completely different and
original context.

Other observations may be made about the results for the four Flavian epics.
The high r values for the epics when paired with the
Georgics (0.160 ≤ r
≥ 0.433) may be influenced by factors discussed in section 3.c.i, but
scholars have begun to interpret the relationship between these texts more
aggressively (Pagán 2015), and our results support this line of inquiry. The
Metamorphoses and Bellum Civile have often been interpreted as important texts
for the Flavian epics; however, although the intensity of text reuse for the
eight relevant pairs was usually higher than average (r ≥ −0.075), it was usually only moderately so, approximately on
par with the intensity of reuse for the epics when paired with the Eclogues, a text rarely argued to be important to
Flavian epic. Again, this does not argue against a strong intertextual
engagement between the Metamorphoses, Bellum Civile, and Flavian epics; it may instead
suggest that future investigations should focus on allusions not signalled
by the obvious text reuse that Tesserae
discovers.

The intertextual relationship between the four Flavian epics has been the
subject of recent study, and this line of inquiry is supported by our
results. The intensity of the Thebaid’s reuse
of the Argonautica was slightly higher than
average, on par with the Thebaid’s reuse of the
Metamorphoses (r =
0.064, 0.037). The intensity of the Achilleid’s
reuse of the Argonautica was much higher than
average, on par with the Achilleid’s reuse of
the Aeneid (r = 0.279,
0.289).While the intertextual relationship between the Thebaid and Argonautica has been
well studied, the relationship between the Achilleid and Argonautica has
not;[27] future work in
this vein could be productive. Unsurprisingly, the intensity of reuse of
Statius’ Thebaid by Statius’ later Achilleid was higher than average (r = 0.141), but it was lower than 11 of the 12
remaining intra-author pairs (Figure 13 and
Table 5). This low reuse cannot be
explained purely by the divergent subject matter and style of Statius’ two
epics: Vergil’s Eclogues and Aeneid are at least as divergent, but had a higher
r value (0.224). Finally, the r value for the pair Achilleid –
Punica was very high (0.410).[28] This was unexpected.
Research on the intertextual relationship between Statius’ and Silius’ works
has focused on the pair Thebaid – Punica,[29] but these results
suggest more attention should be paid to the Achilleid. In all discussion of the Achilleid, however, we should keep in mind that it is much
shorter than the other three Flavian epics; therefore, the considerations
that applied to the Georgics in section 3.c.i
apply here.

iii. Didactic and satiric hexameter

Hardie’s study of the reception of Lucretius makes a strong and well-received
case for the fundamental contribution of the De Rerum
Natura to succeeding poetry from the Augustan poets through
Milton’s Paradise Lost
[Hardie 2009]. No reader would dispute the conceptual and
formal importance of the DRN to the Latin
hexameter tradition. Features of later hexameter poetry such as sententiae, multiple explanations, and similes
from the natural world all bear the marks of the Epicurean poet’s mode of
argumentation. Yet the vocabulary of the DRN
was not mined as extensively as the other foundational works of Republican
and Augustan poetry, as can be seen from our results (centrality = −0.151,
r < 0.000 when paired with 21 of 23 succeeding
target texts; Figure 11). The only positive
r values resulted from pairings with other
didactic works: Vergil’s Georgics (r = 0.230) and Manilius’ Astronomica (r = 0.023). While these
results are consistent with the observed influence of genre on text reuse
(section 3.b), the low r values overall demonstrate
the difference between the importance of Lucretius’ poem as a conceptual
resource and its importance as a verbal resource.

Volk’s study of the Astronomica makes a series
of valuable observations about Manilius’ thematic adaptations of Lucretius,
Vergil, and Ovid [Volk 2009]. Those thematic adaptations were
accompanied by verbal reuse only for Vergil in our results. Vergil’s Georgics yielded the highest reuse intensity (r = 0.342), followed by the Eclogues (r = 0.307). Unexpectedly,
Horace’s Ars Poetica had the next highest r value (0.213). As the Ars is one of the shortest poems in our data set, the
considerations that applied to the Georgics in
section 3.c.i apply here. Yet there may be hitherto unexplored verbal
connections between the poem on composing poetry and the poem of the stars,
likely in the addresses of the didactic narrator. The intensity of reuse of
the DRN was higher than average, but only
negligibly so (r = 0.023). The intensity of text
reuse of the Astronomica by later texts was
low, suggesting a limited influence on the language of subsequent classical
hexameter tradition.

The inclusion of the Satires of Horace, Persius,
and Juvenal (HSat, PSat, JSat) in this study permits
us to begin investigation of the influence of genre on text reuse in Latin
hexameter. As mentioned above (section 3.b), the author signal is a stronger
determinant than the genre signal for intensity of text reuse, as evidenced
by higher r values for pairs of texts written by
Horace than inter-author pairs within the satiric genre.[30] But the importance of
genre was especially marked when pairing epic/panegyric with satiric texts,
where 37 of 39 pairs had lower than average reuse intensity (r < 0.000), including the lowest r values in our data set (Figure
14).[31] These results indicate a strong
separation between the genres, related to satire’s pedestrian vocabulary and
everyday concerns, which contrast with the more elevated style and subject
matter of epic.

iv. Late antiquity

The tremendous influence of Vergil and Ovid on the hexameter poems of late
antiquity has been well recognized in prior scholarship, but has been
typically studied from the perspective of theme, character, and subject. The
present study permits some initial quantification of the intensity of text
reuse between these poems and those occurring earlier in the hexameter
tradition.

Prior scholarship has identified Ausonius’ Mosella as primarily Vergilian in character, with several
secondary influences, but has not heretofore been able to quantify the
nature of Ausonius’ reuse of his predecessors’ texts.[32] In our
study, the intensity of text reuse of Ovid’s Metamorphoses by the Mosella was
markedly higher than average (standardized r >
2.000). This pairing had the highest r value of any
two independently authored texts (r = 1.073), and
second only to Vergil’s reuse of the Georgics
in the Aeneid (r =
1.280). The intensity of reuse of Vergil’s works was decidedly lower (Georgics, r = 0.260; Aeneid, r = 0.115). The
intensity of reuse of Statius’ Achilleid and
Silius Italicus’ Punica was slightly above
average (r = 0.104 and 0.028), but lower than that of
Manilius’ Astronomica and the Ilias Latina (r = 0.130 and
0.120; for the latter, see section 3.c.i). Intensity of reuse was lower than
average (r < 0.000) for Lucretius’ De Rerum Natura, Lucan’s Bellum Civile, Valerius Flaccus’ Argonautica, and Statius’ Thebaid.
The centos entirely composed of phrases adapted from Vergil’s works that
appear in this period represent a new level of engagement with the
foundational texts of the genre [McGill 2005]. Ausonius’
Cento Nuptialis, the best known of the
centos, is available on Tesserae, but was
excluded in this study, since its artificially high reuse rates of Vergil’s
works would have produced extreme outliers that would have distorted our
results.

As observed above (section 3.b), the works of Claudian are evidence for the
strength of the author signal. Four of the top fifteen r values in our data set were derived from pairing works of
Claudian (Hon – Stil, Hon – Gild, Gild – Stil, and Rapt – Hon; 0.461 ≤ r ≥ 0.716).
The lower position of the De Raptu Proserpinae
among the pairings of Claudian’s works (Rapt –
Hon, Rapt –
Gild, Rapt –
Stil; 0.243 ≤ r ≥
0.461) may suggest that Claudian’s self-reuse is strongest among works in a
similar genre (panegyric rather than mythological epic). We are hesitant to
draw firm conclusions, however, about the relative importance of the author
and genre signals with so few data. Claudian’s rates of reuse of his
Augustan predecessors present a similar story to that told in the scholarly
literature [Ware 2012, 9-10]. For instance, Vergil’s
Georgics (r = 0.538)
and Aeneid (r = 0.326)
had high reuse intensity when paired with Claudian’s mythological De Raptu Proserpinae. The intensity of reuse of
Statius’ Achilleid was also high (r = 0.426), which accords with the importance of
Statius as an intermediary between the Augustans and the poets of late
antiquity. As Kaufmann observes, "Claudian, possibly inspired by Ausonius, [was] the
trendsetter for the increased interest in Statius’ poetry by the
later poets"
[Kaufmann 2015]. An unexpected but plausible result is the importance of Lucan’s
Bellum Civile to Claudian’s historical
panegyrics, Gild (r =
0.351) and Hon (r =
0.278).

We also included Juvencus’ Historia Evangelica,
a fourth-century Christian epic, and Corippus’ Johannis, a sixth-century historical epic, in the data set.
Both the Johannis’ high rates of reuse of
Vergil and Claudian and the HE’s low rates of
reuse of classical pagan poetry (with the exception of the Georgics and Ilias
Latina) conform to the expectations set by the scholarly
literature.[33]

4. CONCLUSIONS

We chose to begin by studying a selected corpus of Latin hexameter poems because
relationships between works in this "super-genre" have been
the most closely studied of all intertextual relationships in ancient
literature. We are able to compare the information about the relative rates of
reuse of texts in Table 3 to a long tradition of
qualitative discussion of allusion by Latinists. We provisionally conclude that
a majority of the results conform to the statements typically made by poetry
scholars about the significance of various intertextual relationships in the
Latin hexameter tradition. For instance, the author signal is one of the
strongest determinants of intensity of text reuse, the works of Ovid and Vergil
are the most important verbal resources for the later works of the tradition,
and satiric hexameter is strongly separated from the other hexameter genres in
terms of reuse. If it is accepted that the high level of correlation between our
quantified results and the scholarly tradition’s qualitative assessments
provides a strong vote of confidence for our methodology, then we can begin to
explore the significance of unexpected findings. These include (a) the
importance of Vergil’s Georgics to the later
tradition, (b) the indications of multiple reuse visible in the Ilias Latina, (c) the relatively low reuse of Vergil
by Lucan, Valerius, and Statius, and (d) the intense reuse of Ovid’s Metamorphoses by Ausonius’ Mosella.

This is a first step in algorithmic criticism of the hexameter super-genre [Ramsay 2011]. As observed in the Introduction, Tesserae has some limitations which reflect its
current state of development, and others which reflect the nature of Latin
poetry. In this initial study, we confirmed the value of the lexeme-matching
approach by comparing it to the traditional critical narrative of relationships
among Latin hexameter poems. Our goal is to model a system of relationships
between texts that can frame critics’ discussions of the role of individual
poems within the tradition. As Drucker observes, "on the surface, a model seems static. In reality it is,
like any 'form,' a provocation for a reading, an
intervention, an interpretive act"
[Drucker 2009, 16]. In Drucker’s terms, Tesserae modeling is a
dynamic rather than static approach to textual analysis. New data sets can
easily be constructed, whether by using different Tesserae parameters or changing the texts in the group under analysis.
These future analyses will produce new and different perceptions of the system
of relationships among Latin literary texts in other genres, or between other
genres and the hexameter super-genre.

Intensity of text reuse for 276 pairs of hexameter texts from the 1st century BCE to the 6th century CE, determined by comparing composite counts of high
scoring results in Tesserae searches with
expected counts based on text lengths. Reuse intensity is presented as both
non-standardized and standardized residuals.

Text

Mean r

Georg

0.279

Ilias

0.186

Aen

0.133

Ach

0.117

Stil

0.105

Rapt

0.088

Hon

0.086

Joh

0.078

Met

0.073

Mos

0.057

Pun

0.044

Gild

0.032

BC

0.006

Astr

-0.006

Ecl

-0.018

Arg

-0.036

Theb

-0.096

HE

-0.102

JSat

-0.120

Ars

-0.146

DRN

-0.151

Ep

-0.153

HSat

-0.187

PSat

-0.270

Table 4.

Centrality scores for 24 hexameter texts from the 1st century BCE to the 6th century CE,
determined by calculating for each text the mean text reuse intensity for
all 23 pairs involving that text.

Horace

Vergil

Statius

Claudian

Source

Target

r

Source

Target

r

Source

Target

r

Source

Target

r

Ep

Ars

0.354

Georg

Aen

1.280

Theb

Ach

0.141

Hon

Stil

0.716

HSat

Ep

0.259

Ecl

Georg

0.603

Hon

Gild

0.634

HSat

Ars

0.041

Ecl

Aen

0.224

Gild

Stil

0.575

Rapt

Hon

0.461

Rapt

Gild

0.404

Rapt

Stil

0.324

Table 5.

Intensity of text reuse for pairs of hexameter texts written by the same
author.

Georg

Aen

Ilias

Target

r

Target

r

Target

r

Aen

1.280

Ilias

0.594

Joh

0.663

Met

0.560

Pun

0.540

Gild

0.457

Rapt

0.538

Met

0.350

Pun

0.433

Pun

0.433

Rapt

0.326

Ach

0.396

Joh

0.355

Theb

0.299

Arg

0.389

BC

0.351

Ach

0.289

Theb

0.252

Astr

0.342

Joh

0.269

HE

0.223

Ilias

0.310

Arg

0.255

Stil

0.157

Arg

0.297

Mos

0.115

Rapt

0.152

Gild

0.268

Gild

0.092

Mos

0.120

Mos

0.260

Hon

0.064

JSat

0.045

Hon

0.232

Stil

0.030

Hon

0.036

Theb

0.186

Astr

0.011

HE

0.172

BC

-0.026

Ach

0.160

HE

-0.047

Stil

0.132

JSat

-0.243

JSat

-0.009

Ars

-0.356

Ep

-0.045

PSat

-0.537

Ars

-0.114

PSat

-0.164

Table 6.

Intensity of text reuse for pairs of hexameter texts with Vergil’s Georgics, Vergil’s Aeneid, or the Ilias Latina as
source text.

Intensity of text reuse for 276 pairs of hexameter texts from the 1st century BCE to the 6th century CE, determined by comparing composite counts of high
scoring results in Tesserae searches with expected counts based on a text
lengths. Reuse intensity is sorted chronologically by source text (with the
set of all pairs for comparison).

Figure 12.

Intensity of text reuse for 276 pairs of hexameter texts from the 1st century BCE to the 6th century CE, determined by comparing composite counts of high
scoring results in Tesserae searches with
expected counts based on a text lengths. Reuse intensity is sorted
chronologically by target text (with the set of all pairs for
comparison).

Figure 13.

Intensity of text reuse for pairs of hexameter texts written by the same
author.

Results of Tesserae searches of 276 pairs of
hexameter texts from the 1st century BCE to the
6th century CE, sorted chronologically by
source text. Results include: raw counts of score 7, 8, 9, 10, and 11;
composite counts calculated from the raw counts using a combination of
linear regressions and principal component analysis; and text reuse
intensity, determined by comparing the composite counts with expected counts
based on a text lengths.

Source

Target

C7

C8

C9

C10

C11

Cobs

r

DRN

Ecl

911

193

28

1

0

129.9

-0.134

DRN

HSat

2171

552

100

5

0

380.0

-0.183

Ecl

HSat

201

33

6

0

0

25.0

-0.066

DRN

Georg

2643

894

169

8

0

571.8

0.230

Ecl

Georg

374

98

5

0

0

48.5

0.603

HSat

Georg

583

176

20

0

0

93.0

0.037

DRN

Ep

1414

358

70

4

0

256.7

-0.139

Ecl

Ep

130

27

1

0

0

14.5

-0.171

HSat

Ep

490

126

19

0

0

75.4

0.259

Georg

Ep

413

114

6

0

0

55.3

-0.045

DRN

Aen

11060

3350

790

92

0

2755.7

-0.015

Ecl

Aen

1222

250

53

4

0

204.4

0.224

HSat

Aen

2638

674

111

3

0

432.7

-0.243

Georg

Aen

4150

1276

275

42

4

1974.8

1.280

Ep

Aen

1658

420

56

5

0

276.9

-0.216

DRN

Ars

407

74

16

2

0

68.6

-0.052

Ecl

Ars

39

3

0

0

0

2.9

-0.370

HSat

Ars

151

23

1

0

0

14.8

0.041

Georg

Ars

140

16

1

0

0

12.6

-0.114

Ep

Ars

134

22

0

0

0

12.6

0.354

Aen

Ars

539

85

15

1

0

71.3

-0.356

DRN

Met

12958

3726

827

100

0

3036.4

-0.164

Ecl

Met

1482

325

55

12

0

288.5

0.323

HSat

Met

3107

780

112

7

0

509.6

-0.326

Georg

Met

4460

1415

251

28

1

1228.6

0.560

Ep

Met

2052

545

67

5

0

338.6

-0.261

Aen

Met

21610

6172

1364

250

7

7156.5

0.350

Ars

Met

681

83

15

2

0

85.3

-0.112

DRN

Astr

5380

1458

331

10

0

1030.2

0.023

Ecl

Astr

501

93

24

1

0

79.9

0.307

HSat

Astr

1090

282

31

0

0

156.9

-0.236

Georg

Astr

1578

473

75

1

0

278.1

0.342

Ep

Astr

860

205

15

0

0

110.4

-0.114

Aen

Astr

6658

1763

437

35

0

1435.2

0.011

Ars

Astr

213

58

8

0

0

33.3

0.213

Met

Astr

8646

2366

466

39

0

1738.9

-0.064

DRN

PSat

601

84

16

2

0

81.9

-0.316

Ecl

PSat

56

7

0

0

0

4.8

-0.316

HSat

PSat

196

45

6

0

0

27.4

0.210

Georg

PSat

158

34

2

0

0

18.7

-0.164

Ep

PSat

116

25

1

0

0

13.3

-0.040

Aen

PSat

735

131

21

0

0

92.6

-0.537

Ars

PSat

37

4

0

0

0

3.0

0.008

Met

PSat

797

161

22

6

0

141.6

-0.379

Astr

PSat

305

59

2

0

0

32.8

-0.468

DRN

BC

8160

2318

464

22

0

1591.4

-0.297

Ecl

BC

801

146

23

2

0

114.3

-0.089

HSat

BC

1983

424

58

6

0

304.8

-0.326

Georg

BC

2876

794

140

18

0

596.6

0.351

Ep

BC

1437

278

46

1

0

197.2

-0.288

Aen

BC

13863

3157

815

99

0

2942.2

-0.026

Ars

BC

398

56

10

1

0

51.7

-0.100

Met

BC

16737

3936

966

131

6

5000.8

0.238

Astr

BC

6009

1465

239

21

0

1045.0

0.048

PSat

BC

564

79

12

1

0

68.4

-0.300

DRN

Ilias

1089

254

39

3

0

177.4

-0.017

Ecl

Ilias

138

19

1

0

0

13.2

0.224

HSat

Ilias

239

56

3

0

0

29.3

-0.195

Georg

Ilias

372

111

2

0

0

48.3

0.310

Ep

Ilias

150

39

0

0

0

17.4

-0.245

Aen

Ilias

2361

670

112

10

0

460.9

0.594

Ars

Ilias

59

3

0

0

0

4.1

-0.171

Met

Ilias

2497

662

74

14

1

681.8

0.719

Astr

Ilias

732

161

17

0

0

95.2

0.125

PSat

Ilias

63

7

0

0

0

5.2

-0.406

BC

Ilias

1387

294

25

4

0

195.8

0.028

DRN

Arg

5904

1416

351

15

0

1102.3

-0.283

Ecl

Arg

693

116

13

1

0

85.1

-0.003

HSat

Arg

1419

329

31

1

0

192.6

-0.404

Georg

Arg

2341

584

108

2

0

386.1

0.297

Ep

Arg

951

211

34

0

0

136.0

-0.279

Aen

Arg

12214

3071

647

99

0

2660.2

0.255

Ars

Arg

268

33

6

0

0

28.8

-0.304

Met

Arg

12279

2899

622

75

1

2677.2

-0.006

Astr

Arg

3871

867

171

3

0

606.8

-0.114

PSat

Arg

434

63

12

0

0

51.1

-0.211

BC

Arg

8571

1837

391

48

0

1597.4

0.035

Ilias

Arg

1150

237

30

1

0

155.5

0.389

DRN

Theb

9682

2443

520

44

0

1901.1

-0.363

Ecl

Theb

1106

175

29

1

0

138.1

-0.145

HSat

Theb

2400

551

67

6

0

366.3

-0.387

Georg

Theb

3433

965

144

14

0

645.8

0.186

Ep

Theb

1496

372

32

2

0

214.0

-0.451

Aen

Theb

18667

4816

1166

190

3

5196.9

0.299

Ars

Theb

515

57

7

0

0

49.4

-0.390

Met

Theb

19745

5002

1015

165

4

5220.1

0.037

Astr

Theb

6224

1506

257

16

0

1053.1

-0.189

PSat

Theb

698

113

11

1

0

82.7

-0.354

BC

Theb

14282

3067

631

85

0

2674.1

-0.075

Ilias

Theb

1818

380

45

3

0

253.6

0.252

Arg

Theb

11371

2503

535

45

0

2032.8

0.064

DRN

Ach

1117

260

42

3

0

183.4

-0.091

Ecl

Ach

128

22

0

0

0

12.3

0.047

HSat

Ach

254

81

3

0

0

35.8

-0.102

Georg

Ach

428

83

3

0

0

46.2

0.160

Ep

Ach

188

43

0

0

0

20.5

-0.188

Aen

Ach

2196

511

87

8

0

378.1

0.289

Ars

Ach

50

7

0

0

0

4.4

-0.187

Met

Ach

2330

639

78

7

0

399.3

0.077

Astr

Ach

760

166

17

0

0

97.9

0.047

PSat

Ach

60

14

0

0

0

6.6

-0.273

BC

Ach

1607

303

48

4

0

233.5

0.097

Ilias

Ach

234

36

0

0

0

21.5

0.396

Arg

Ach

1304

297

31

2

0

185.1

0.279

Theb

Ach

2283

517

58

2

0

317.8

0.141

DRN

Pun

12722

3892

907

105

0

3171.5

-0.092

Ecl

Pun

1312

261

45

7

0

222.9

0.093

HSat

Pun

3151

839

108

13

0

559.1

-0.205

Georg

Pun

4728

1489

245

32

0

1052.0

0.433

Ep

Pun

1803

460

79

3

0

304.7

-0.338

Aen

Pun

26063

7011

1720

323

7

8415.5

0.540

Ars

Pun

642

109

15

0

0

76.3

-0.195

Met

Pun

24950

6621

1564

284

5

7407.3

0.146

Astr

Pun

8545

2134

439

30

0

1597.4

-0.013

PSat

Pun

891

150

28

1

0

119.1

-0.230

BC

Pun

18677

4366

957

147

1

4161.6

0.126

Ilias

Pun

2581

582

70

6

0

386.6

0.433

Arg

Pun

14178

3236

650

68

0

2618.2

0.076

Theb

Pun

22806

5275

1076

165

2

5062.7

0.057

Ach

Pun

2631

614

91

7

0

423.9

0.410

DRN

JSat

3685

955

188

5

0

645.5

-0.330

Ecl

JSat

413

83

9

0

0

51.4

-0.021

HSat

JSat

1167

292

37

1

0

175.9

-0.007

Georg

JSat

1104

329

31

1

0

174.6

-0.009

Ep

JSat

705

219

21

0

0

110.7

0.003

Aen

JSat

5113

1252

299

19

0

993.2

-0.243

Ars

JSat

229

39

4

0

0

25.9

0.078

Met

JSat

6383

1685

328

37

0

1305.5

-0.236

Astr

JSat

2253

554

91

3

0

363.6

-0.139

PSat

JSat

343

79

7

0

0

44.4

0.137

BC

JSat

4549

1061

211

10

0

773.3

-0.203

Ilias

JSat

465

107

17

0

0

67.7

0.045

Arg

JSat

2895

682

118

4

0

462.5

-0.303

Theb

JSat

4733

1232

170

13

0

800.1

-0.434

Ach

JSat

559

115

10

0

0

67.9

-0.068

Pun

JSat

6378

1625

296

26

0

1190.6

-0.298

DRN

HE

3353

883

132

4

0

548.0

-0.221

Ecl

HE

330

56

5

0

0

36.5

-0.089

HSat

HE

769

190

19

0

0

105.8

-0.242

Georg

HE

975

313

33

0

0

159.3

0.172

Ep

HE

513

114

5

0

0

60.1

-0.336

Aen

HE

4656

1236

255

19

0

919.3

-0.047

Ars

HE

164

27

3

0

0

18.5

0.013

Met

HE

5307

1420

235

20

0

984.3

-0.246

Astr

HE

1916

533

61

0

0

290.7

-0.090

PSat

HE

197

33

0

0

0

18.7

-0.454

BC

HE

3518

851

138

8

0

581.4

-0.215

Ilias

HE

484

106

10

0

0

61.6

0.223

Arg

HE

2731

614

121

0

0

415.7

-0.137

Theb

HE

4047

989

184

10

0

701.3

-0.293

Ach

HE

448

87

6

0

0

51.3

-0.076

Pun

HE

5321

1403

262

26

0

1046.0

-0.154

JSat

HE

1386

361

47

1

0

213.9

-0.273

DRN

Mos

377

86

14

1

0

61.2

-0.111

Ecl

Mos

54

3

0

0

0

3.8

-0.058

HSat

Mos

87

20

1

0

0

10.5

-0.253

Georg

Mos

159

28

2

0

0

17.4

0.260

Ep

Mos

75

14

0

0

0

7.5

-0.121

Aen

Mos

623

114

28

3

0

108.2

0.115

Ars

Mos

24

1

0

0

0

1.6

-0.131

Met

Mos

814

135

26

6

1

368.5

1.073

Astr

Mos

314

59

5

0

0

36.3

0.130

PSat

Mos

31

3

0

0

0

2.5

-0.185

BC

Mos

463

76

19

0

0

62.7

-0.142

Ilias

Mos

50

12

0

0

0

5.6

0.120

Arg

Mos

357

50

4

0

0

35.7

-0.290

Theb

Mos

612

95

20

1

0

82.8

-0.128

Ach

Mos

68

10

0

0

0

6.1

0.104

Pun

Mos

755

133

21

5

0

125.6

0.028

JSat

Mos

191

43

5

0

0

25.6

-0.094

HE

Mos

165

35

2

0

0

19.3

-0.079

DRN

Rapt

1028

235

49

2

0

173.4

-0.111

Ecl

Rapt

117

20

0

0

0

11.2

-0.009

HSat

Rapt

218

49

0

0

0

23.5

-0.485

Georg

Rapt

379

126

15

0

0

65.1

0.538

Ep

Rapt

150

41

3

0

0

20.8

-0.133

Aen

Rapt

1674

494

83

14

0

378.1

0.326

Ars

Rapt

49

5

0

0

0

3.9

-0.272

Met

Rapt

1971

548

97

9

0

389.8

0.089

Astr

Rapt

657

151

17

0

0

88.6

-0.016

PSat

Rapt

62

5

0

0

0

4.7

-0.579

BC

Rapt

1458

334

47

9

0

262.3

0.250

Ilias

Rapt

158

32

0

0

0

16.3

0.152

Arg

Rapt

1099

224

31

0

0

144.4

0.067

Theb

Rapt

1844

500

53

5

0

302.6

0.128

Ach

Rapt

207

45

2

0

0

24.0

0.426

Pun

Rapt

2074

573

76

11

0

392.9

0.128

JSat

Rapt

464

131

15

0

0

71.1

-0.113

HE

Rapt

430

102

11

0

0

58.6

-0.010

Mos

Rapt

62

5

1

0

0

5.7

0.153

DRN

Hon

565

124

27

1

0

93.6

-0.042

Ecl

Hon

51

5

0

0

0

4.1

-0.341

HSat

Hon

125

18

2

0

0

13.2

-0.376

Georg

Hon

204

42

3

0

0

24.1

0.232

Ep

Hon

94

19

1

0

0

10.7

-0.117

Aen

Hon

910

182

41

2

0

146.7

0.064

Ars

Hon

28

1

0

0

0

1.8

-0.351

Met

Hon

992

260

29

5

0

175.8

-0.022

Astr

Hon

407

70

8

0

0

47.1

0.036

PSat

Hon

30

6

0

0

0

3.1

-0.315

BC

Hon

920

160

22

4

0

135.9

0.278

Ilias

Hon

88

10

0

0

0

7.3

0.036

Arg

Hon

538

96

11

1

0

69.7

0.024

Theb

Hon

972

200

28

2

0

141.3

0.052

Ach

Hon

112

16

0

0

0

10.0

0.238

Pun

Hon

1168

247

31

5

0

185.0

0.060

JSat

Hon

300

67

10

0

0

42.3

0.053

HE

Hon

244

43

3

0

0

26.7

-0.112

Mos

Hon

44

5

0

0

0

3.6

0.395

Rapt

Hon

104

27

0

0

0

12.0

0.461

DRN

Gild

387

65

9

0

0

45.8

-0.484

Ecl

Gild

30

5

0

0

0

2.8

-0.422

HSat

Gild

126

19

0

0

0

11.5

-0.243

Georg

Gild

162

39

1

0

0

19.1

0.268

Ep

Gild

57

17

0

0

0

7.1

-0.253

Aen

Gild

773

151

24

2

0

114.9

0.092

Ars

Gild

15

0

0

0

0

0.9

-0.834

Met

Gild

775

192

23

3

0

129.5

-0.055

Astr

Gild

304

47

4

0

0

32.0

-0.077

PSat

Gild

28

2

0

0

0

2.1

-0.444

BC

Gild

580

133

23

4

0

111.3

0.351

Ilias

Gild

77

18

0

0

0

8.5

0.457

Arg

Gild

430

82

14

0

0

57.1

0.097

Theb

Gild

727

130

17

1

0

94.2

-0.081

Ach

Gild

68

13

1

0

0

7.8

0.263

Pun

Gild

957

163

31

2

0

135.1

0.018

JSat

Gild

222

42

6

0

0

28.2

-0.081

HE

Gild

202

44

3

0

0

24.5

0.075

Mos

Gild

34

0

0

0

0

2.0

0.042

Rapt

Gild

100

13

0

0

0

8.7

0.404

Hon

Gild

59

8

0

0

0

5.2

0.634

DRN

Stil

1074

282

49

1

0

180.3

-0.170

Ecl

Stil

137

26

0

0

0

13.7

0.094

HSat

Stil

257

68

3

0

0

33.0

-0.244

Georg

Stil

353

96

6

0

0

47.8

0.132

Ep

Stil

199

47

2

0

0

24.0

-0.090

Aen

Stil

1761

411

73

7

0

310.4

0.030

Ars

Stil

66

15

0

0

0

7.2

0.228

Met

Stil

2073

597

84

10

0

400.0

0.017

Astr

Stil

731

159

21

0

0

98.7

-0.007

PSat

Stil

77

16

0

0

0

8.0

-0.139

BC

Stil

1645

357

59

9

0

290.2

0.253

Ilias

Stil

177

35

0

0

0

18.0

0.157

Arg

Stil

1120

226

22

1

0

143.3

-0.039

Theb

Stil

1836

395

60

6

0

291.8

-0.006

Ach

Stil

226

45

1

0

0

24.1

0.331

Pun

Stil

2204

544

73

6

0

359.4

-0.059

JSat

Stil

592

143

12

0

0

78.1

-0.117

HE

Stil

470

117

11

0

0

64.3

-0.016

Mos

Stil

77

11

0

0

0

6.9

0.247

Rapt

Stil

193

53

0

0

0

23.0

0.324

Hon

Stil

133

38

0

0

0

16.2

0.716

Gild

Stil

100

21

0

0

0

10.5

0.575

DRN

Joh

4842

1393

306

13

0

978.6

-0.101

Ecl

Joh

524

99

5

0

0

57.3

-0.098

HSat

Joh

1052

287

30

0

0

154.9

-0.322

Georg

Joh

1677

473

94

1

0

302.8

0.355

Ep

Joh

742

164

19

0

0

98.4

-0.301

Aen

Joh

9050

2482

550

59

0

1997.9

0.269

Ars

Joh

207

29

2

0

0

20.4

-0.348

Met

Joh

9569

2604

515

29

0

1831.5

-0.085

Astr

Joh

3184

870

154

4

0

557.3

0.101

PSat

Joh

250

46

5

0

0

29.7

-0.453

BC

Joh

7466

1754

336

23

0

1303.1

0.132

Ilias

Joh

977

212

29

3

0

151.6

0.663

Arg

Joh

4622

1069

171

9

0

733.0

-0.030

Theb

Joh

7759

1900

303

22

0

1313.5

-0.125

Ach

Joh

881

194

27

1

0

127.4

0.375

Pun

Joh

11414

2598

525

43

0

2034.1

0.051

JSat

Joh

2486

594

106

3

0

401.0

-0.104

HE

Joh

2321

639

105

8

0

432.1

0.267

Mos

Joh

263

43

10

0

0

34.8

0.243

Rapt

Joh

845

198

21

0

0

114.0

0.302

Hon

Joh

472

89

11

0

0

58.1

0.372

Gild

Joh

402

74

6

0

0

45.7

0.427

Stil

Joh

862

182

24

0

0

114.4

0.199

Table 9.

Results of Tesserae searches of 276 pairs of
hexameter texts from the 1st century BCE to the
6th century CE, sorted chronologically by
target text. Results include: raw counts of score 7, 8, 9, 10, and 11;
composite counts calculated from the raw counts using a combination of
linear regressions and principal component analysis; and text reuse
intensity, determined by comparing the composite counts with expected counts
based on a text lengths.

Notes

[3] Because Latin is a
highly inflected language, the same lexeme may occur in many different inflected
forms. For example, percutio may appear as
percussus ("struck"), percutimus ("we strike"), percusserant ("they had struck"), etc.
Traditional literary interpretation may privilege specific morphological forms,
such as the opening words of Vergil’s Aeneid
(arma uirumque), which are frequently
adapted by later poets, but more often the various inflected forms of a lexeme
may be considered to be the same. Tesserae converts
all inflected forms to a single lemma (e.g., percussus and percussum are
treated as percutio) and so does not permit
analysis of individual inflected forms.

[4] See section 2.b for discussion of the
scoring system.

[5] Recent commentaries (such as [Steiniger 2005], [Micozzi 2007], and [Parkes 2012]) note the
verbal parallel with Aeneid 7.550, but do not offer
a literary interpretation of the link. Their reticence is symptomatic of the
scholarly tendency to privilege certain allusions (here, Aeneid 9.197) over others in interpretation. The impartial
automatic searches of Tesserae encourage an
interpretive style that is both less hierarchical and less committed to
authorial intention.

[6] The dates of texts mostly follow
those found in Brill’s New Pauly, and
depart in some cases from the dates used by the Tesserae to assign source and target text status for each
pair (http://tesserae.caset.buffalo.edu/blog/authors−and−text−dates/).
Where necessary, we manually corrected for the switched source and
target. Some dates are uncertain; see, e.g., [Zissos 2008, xiv–xvii] on Valerius Flaccus’ Argonautica, or [Gruzelier 1993, xviii–xix] on Claudian’s De Raptu
Proserpinae. Alternative datings would affect our results in
some cases, since the calculation of the variable cexp depends on which text in a pair
is considered the source and which the target. But the overall effect of
any plausible change in dating would be small.

[7] The Tesserae repository is extensive but not complete. Relevant
hexameter texts unavailable for the study at this writing include, for
example, Ennius’ Annales, the Appendix Vergiliana, the Eclogues of Calpurnius Siculus, and the various Latin
versions of Aratus’ Phaenomena.

[8] We included Claudian’s De
Raptu Proserpinae because it is an important text and
because its pentameter preface is short compared to the text as a whole
(69 out of 6991 words), and therefore unlikely to noticeably affect our
results.

[10] False lemma matches also sometimes occur, such
as Vergil, Georgics 4.308 ossibus umor ~ Statius,
Thebaid 4.698 ora … umor.
Here ossibus ("bones") and
ora ("faces") are inflected
forms of two different lexemes, both of which share the lemma os. Since such false matches occur
infrequently, we did not expect them to affect the results
significantly.

We omitted the intercepts, which provide no useful
information, and thus obtained a formula for a composite count:

Figure 3.

[13] The first principal component had weights

Figure 4.

This led to, in original scale, the composite count (which
accounts for 90.1% of the total variability):

Figure 5.

Further rescaling it such that the weight for C9 became 1, we
obtained:

Figure 6.

[14] This
model was the best of several considered, namely:

Figure 8.

[15] The r values are also sorted chronologically by
source and target in Table 8 and 9. Standardized residuals have been adjusted
by the standard deviation of the entire set in order to detect
statistical outliers. Standardized residuals greater than |2| are
normally considered unusual; standardized residual greater than |3| are
normally considered statistical outliers.

[17] Jockers observes, "the strength of the author signals in this
experiment in fact trumps the signals of individual texts —
something intuition does not prepare us for. The classifier
[program] is more likely to identify the author of a given text
segment than it is to correctly assign that same text segment to
its novel of origin."
[Jockers 2013, 93]

[18] The didactic genre comprised: DRN, Georg, and
Astr. The epic/panegyric genre
comprised: Aen, Met, BC, Ilias, Arg, Theb, Ach, Pun, Rapt, Hon, Gild, Stil, and Joh.
The satiric genre comprised: HSat, PSat, and JSat.
This partitioning excludes five texts (Ecl,
Ep, Ars,
HE, Mos)
that do not fit into any of the three genres. Including Horace’s Epistles and Ars
Poetica in the satiric genre would not alter our
conclusions: in fact, the lowest r value in our
data set would then comprise an epic/panegyric–satric pair, Ars – Gild (r = −0.834).

[20] The average
Cobs value for the
Aeneid paired with all subsequent
target texts is 1876.6, compared to 284.6 for the Georgics.

[21] Its influence grew later on: the text
is quoted in the late antique commentary on the Thebaid ascribed to Lactantius Placidus, and became popular
in the Middle Ages [Curtius 1953, 49-51].

[22] This is consistent with scholarly observation; see
New Pauly s.v. Ilias Latina
[Courtney 2016].

[23] For
example, see the discussion of the "many
mouths" topos [Gowers 2005].

[24] For
discussion of the Flavian poets’ gradual return to scholarly favor, see
[Dominik 2010].

[25] Given the low residual, it is
remarkable that Tesserae searches reported
in [Coffee et al. 2012] identified 25% more interpretively
significant instances of verbal reuse in the pair Aeneid – Bellum Civile 1 than
the standard philological commentaries. Similar studies for pairs with
more intense text reuse (e.g., Aeneid –
Metamorphoses) would presumably be
even more successful.

[26] E.g.,

Compared with other writers of Latin epic,
[Silius] tends to eschew signposting his intertexts by the
technique of "quotation", that is, by repeating complete
phrases or other word collocations from earlier poems. He
prefers to signal the intertextual connection by alternative
means, in particular, by coincidence of situation and detail
rather than wording and, occasionally, by more explicit
hints.
[Wilson 2004, 225]

[27] Parkes on the Achilleid and
Argonautica is an exception [Parkes 2009]. For the Thebaid
and Argonautica, see [Lovatt 2015], with bibliography.

[28] The
relative dating of these two epics is uncertain. This study has treated
the Achilleid as the source, but the two
epics may well have been composed concurrently and influenced one
another [Ripoll 2015].

[29] Marks argues for "bi-directional influence" between the two
works [Marks 2014].

[33] Hofmann (New Pauly s.v.
Corippus, Flavius Cresconius) calls
Corippus "the last great practitioner of the Roman
epic… in his use of language and his narrative skill," and
cites Vergil and Claudian as the poet’s primary classical influences.
Juvencus’ Historia Evangelica differs from
all other texts in the data set due to its Biblical subject matter, and
it should accordingly come as no surprise that exhibits both low rates
of reuse and low centrality. Schmidt (New
Pauly s.v. Iuvencus, C. Vettius
Aquilinus) lists only Vergil as a relevant source for
Juvencus. See [Green 2006, 11–14], who observes
"roughly speaking, allusions to Vergil outnumber
allusions to all other writers combined by at least five to
one" (11 n. 63).